Method and apparatus for calibrating a plurality of cameras

ABSTRACT

A camera calibration method includes obtaining a plurality of images of surroundings of a vehicle captured by a plurality of cameras, setting a region of interest (ROI) in each of the images, detecting one or more feature points of the set ROIs, matching a first feature point of a first ROI and a second feature point of a second ROI based on the detected feature points, calculating a first bird-view coordinate of the first feature point and a second bird-view coordinate of the second feature point, and calibrating the cameras by adjusting an extrinsic parameter of each of the cameras based on an error between the first bird-view coordinate and the second bird-view coordinate.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the priority benefit of Korean PatentApplication No. 10-2019-0078562 filed on Jul. 1, 2019, in the KoreanIntellectual Property Office, the disclosure of which is incorporatedherein by reference for all purposes.

BACKGROUND 1. Field

One or more example embodiments relate to a technology for calibratingcameras of a vehicle, and more particularly, to a technology forcalibrating cameras of a vehicle while the vehicle is traveling.

2. Description of Related Art

As an image capturing and processing technology develops, recentlyproduced vehicles are equipped with cameras, and a top view system thatassists a driver or a user in driving a vehicle or assists a vehicle intraveling is provided to a user or a vehicle. The top view system maygenerate a top-view image or a bird's-eye view image (hereinafter simply“bird-view image”) using a plurality of images captured by a pluralityof cameras. The bird-view image may provide a driver with an elevatedtop view of a vehicle that is viewed from above, and thus contribute tocompletely removing a blind spot on a front side, a rear side, a leftside, and/or a right side of the vehicle.

SUMMARY

An aspect provides a method and device for calibrating a plurality ofcameras.

Another aspect provides a method and device for calibrating a pluralityof cameras while a vehicle is traveling.

According to an example embodiment, there is provided a cameracalibration method to be performed by a processor provided in a vehicle.The camera calibration method includes obtaining a plurality of imagesof surroundings of the vehicle captured by a plurality of cameras of thevehicle, setting a region of interest (ROI) in each of the images amongwhich a first ROI of a first image and a second ROI of a second imageinclude a common region, detecting one or more feature points of the setROIs, matching a first feature point of the first ROI and a secondfeature point of the second ROI based on the detected feature points,calculating a first bird-view coordinate of the first feature point anda second bird-view coordinate of the second feature point, andcalibrating the cameras by adjusting an extrinsic parameter of each ofthe cameras based on an error between the first bird-view coordinate andthe second bird-view coordinate.

The cameras may include at least two of a left camera configured tocapture an image of a left-side view from the vehicle, a right cameraconfigured to capture an image of a right-side view from the vehicle, afront camera configured to capture an image of a front-side view fromthe vehicle, or a rear camera configured to capture an image of arear-side view from the vehicle.

The extrinsic parameter of each of the cameras may include rotationinformation and position information of each of the cameras.

The feature points may be associated with an object on a road on whichthe vehicle is traveling.

When the first image is a front-side view image and the second image isa left-side view image, the setting of the ROI in each of the images mayinclude setting the ROI in each of the images such that the first ROIincludes a leftmost side of the front-side view image and the second ROIincludes a rightmost side of the left-side view image.

The camera calibration method may further include generating partialimages by cropping the set ROIs. The detecting of the feature points ofthe ROIs may include detecting the one or more feature points of theROIs based on the generated partial images.

When the number of matching feature points of the first ROI and thesecond ROI is greater than or equal to a preset number, the calculatingof the first bird-view coordinate of the first feature point and thesecond bird-view coordinate of the second feature point may includecalculating the first bird-view coordinate and the second bird-viewcoordinate.

The calibrating of the cameras may include calculating a first errorbetween the first bird-view coordinate and the second bird-viewcoordinate, and adjusting an extrinsic parameter of at least one of thecameras such that the first error is minimized.

When the extrinsic parameter of the at least one of the cameras isadjusted, the calculating of the first bird-view coordinate of the firstfeature point and the second bird-view coordinate of the second featurepoint may include calculating a first adjusted bird-view coordinate ofthe first feature point and a second adjusted bird-view coordinate ofthe second feature point based on the adjusted extrinsic parameter. Thecalibrating of the cameras may include calculating a first adjustederror between the first adjusted bird-view coordinate and the secondadjusted bird-view coordinate.

When the first error is less than a preset threshold value, thecalibrating of the cameras may further include terminating thecalibrating of the cameras.

According to another example embodiment, there is provided an electronicdevice provided in a vehicle to calibrate a plurality of cameras. Theelectronic device includes a memory in which a program of calibratingthe cameras is recorded, and a processor configured to perform theprogram. The program may include obtaining a plurality of images ofsurroundings of the vehicle captured by the cameras of the vehicle,setting an ROI in each of the images among which a first ROI of a firstimage and a second ROI of a second image include a common region,detecting one or more feature points of the set ROIs, matching a firstfeature point of the first ROI and a second feature point of the secondROI based on the detected feature points, calculating a first bird-viewcoordinate of the first feature point and a second bird-view coordinateof the second feature point, and calibrating the cameras by adjusting anextrinsic parameter of each of the cameras based on an error between thefirst bird-view coordinate and the second bird-view coordinate.

The cameras may include at least two of a left camera configured tocapture an image of a left-side view from the vehicle, a right cameraconfigured to capture an image of a right-side view from the vehicle, afront camera configured to capture an image of a front-side view fromthe vehicle, or a rear camera configured to capture an image of arear-side view from the vehicle.

The extrinsic parameter of each of the cameras may include rotationinformation and position information of each of the cameras.

The feature points may be associated with an object on a road on whichthe vehicle is traveling.

When the first image is a front-side view image and the second image isa left-side view image, the setting of the ROI in each of the images mayinclude setting the ROI in each of the images such that the first ROIincludes a leftmost side of the front-side view image and the second ROIincludes a rightmost side of the left-side view image.

The program may further include generating partial images by croppingthe set ROIs. The detecting of the feature points of the ROIs mayinclude detecting the feature points of the ROIs based on the generatedpartial images.

When the number of matching feature points of the first ROI and thesecond ROI is greater than or equal to a preset number, the calculatingof the first bird-view coordinate of the first feature point and thesecond bird-view coordinate of the second feature point may includecalculating the first bird-view coordinate and the second bird-viewcoordinate.

The calibrating of the cameras may include calculating a first errorbetween the first bird-view coordinate and the second bird-viewcoordinate, and adjusting an extrinsic parameter of at least one of thecameras such that the first error is minimized.

When the extrinsic parameter of the at least one of the cameras isadjusted, the calculating of the first bird-view coordinate of the firstfeature point and the second bird-view coordinate of the second featurepoint may include calculating a first adjusted bird-view coordinate ofthe first feature point and a second adjusted bird-view coordinate ofthe second feature point based on the adjusted extrinsic parameter. Thecalibrating of the cameras may include calculating a first adjustederror between the first adjusted bird-view coordinate and the secondadjusted bird-view coordinate.

When the first error is less than a preset threshold value, thecalibrating of the cameras may include terminating the calibrating ofthe cameras.

According to still another example embodiment, there is provided acamera calibration method to be performed by a processor provided in avehicle. The camera calibration method includes obtaining a plurality ofimages of surroundings of the vehicle captured by a plurality of camerasof the vehicle, determining a partial region of each of the images basedon an ROI that is set in advance with respect to a bird's eye viewpoint,generating a plurality of partial bird-view images by transforming thedetermined partial regions by the bird's eye viewpoint, in which a firstpartial bird-view image among the partial bird-view images correspondsto a first ROI and a second partial bird-view image among the partialbird-view images corresponds to a second ROI and the first ROI and thesecond ROI include a common region, detecting a first feature point ofthe first partial bird-view image and a second feature point of thesecond partial bird-view image, matching the first feature point and thesecond feature point, and calibrating the cameras by adjusting anextrinsic parameter of each of the cameras based on an error between afirst bird-view coordinate of the first feature point and a secondbird-view coordinate of the second feature point.

The cameras may include at least two of a left camera configured tocapture an image of a left-side view from the vehicle, a right cameraconfigured to capture an image of a right-side view from the vehicle, afront camera configured to capture an image of a front-side view fromthe vehicle, or a rear camera configured to capture an image of arear-side view from the vehicle.

When the number of matching feature points of the first partialbird-view image and the second partial bird-view image is greater thanor equal to a preset number, the calibrating of the cameras by adjustingthe extrinsic parameter of each of the cameras based on the errorbetween the first bird-view coordinate of the first feature point andthe second bird-view coordinate of the second feature point may includecalibrating the cameras.

The calibrating of the cameras by adjusting the extrinsic parameter ofeach of the cameras based on the error between the first bird-viewcoordinate of the first feature point and the second bird-viewcoordinate of the second feature point may include calculating a firsterror between the first bird-view coordinate and the second bird-viewcoordinate, and adjusting an extrinsic parameter of at least one of thecameras such that the first error is minimized.

According to yet another example embodiment, there is provided anelectronic device provided in a vehicle to calibrate a plurality ofcameras. The electronic device includes a memory in which a program ofcalibrating the cameras is recorded, and a processor configured toperform the program. The program may include obtaining a plurality ofimages of surroundings of the vehicle captured by the cameras of thevehicle, determining a partial region of each of the images based on anROI that is set in advance with respect to a bird's eye viewpoint,generating a plurality of partial bird-view images by transforming thedetermined partial regions by the bird's eye viewpoint, in which a firstpartial bird-view image among the partial bird-view images correspondsto a first ROI and a second partial bird-view image among the partialbird-view images corresponds to a second ROI and the first ROI and thesecond ROI include a common region, detecting a first feature point ofthe first partial bird-view image and a second feature point of thesecond partial bird-view image, matching the first feature point and thesecond feature point, and calibrating the cameras by adjusting anextrinsic parameter of each of the cameras based on an error between afirst bird-view coordinate of the first feature point and a secondbird-view coordinate of the second feature point.

Additional aspects of example embodiments will be set forth in part inthe description which follows and, in part, will be apparent from thedescription, or may be learned by practice of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects, features, and advantages of the presentdisclosure will become apparent and more readily appreciated from thefollowing description of example embodiments, taken in conjunction withthe accompanying drawings of which:

FIG. 1 is a diagram illustrating an example of a bird-view image of avehicle according to an example embodiment;

FIGS. 2 and 3 are diagrams illustrating examples of rotation informationand position information of extrinsic parameters of cameras according toan example embodiment;

FIG. 4 is a diagram illustrating an example of an electronic deviceconfigured to calibrate a plurality of cameras according to an exampleembodiment;

FIG. 5 is a flowchart illustrating an example of a method of calibratinga plurality of cameras according to an example embodiment;

FIG. 6 is a diagram illustrating examples of regions of interest (ROIs)according to an example embodiment;

FIG. 7 is a diagram illustrating an example of a method of extractingand matching feature points according to an example embodiment;

FIG. 8 is a flowchart illustrating an example of a method of calibratinga plurality of cameras by adjusting an extrinsic parameter of each ofthe cameras according to an example embodiment;

FIG. 9 is a diagram illustrating an example of an error betweenbird-view coordinates of matching feature points according to an exampleembodiment;

FIG. 10 illustrates examples of partial bird-view images generated whena plurality of cameras are desirably calibrated according to an exampleembodiment;

FIG. 11 illustrates examples of partial bird-view images generated whena plurality of cameras are not desirably calibrated according to anexample embodiment;

FIG. 12 is a flowchart illustrating an example of a method ofcalculating an adjusted error between bird-view coordinates based on anadjusted extrinsic parameter according to an example embodiment;

FIG. 13 is a flowchart illustrating an example of a method of adjustingan extrinsic parameter of a camera such that an error is minimizedaccording to an example embodiment;

FIG. 14 is a flowchart illustrating an example of a method ofcalibrating a plurality of cameras according to another exampleembodiment;

FIGS. 15 and 16 are diagrams illustrating examples of a partial regionof an image determined based on a preset ROI in a bird-view imageaccording to an example embodiment;

FIG. 17 is a diagram illustrating an example of a method of extractingand matching feature points of partial bird-view images according to anexample embodiment; and

FIG. 18 is a flowchart illustrating an example of a method ofcalibrating a plurality of cameras by adjusting an extrinsic parameterof each of the cameras based on an error between a first bird-viewcoordinate and a second bird-view coordinate according to an exampleembodiment.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known in the art may be omitted forincreased clarity and conciseness.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the,” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises,” “comprising,”“includes,” and/or “including,” when used herein, specify the presenceof stated features, integers, operations, elements, and/or components,but do not preclude the presence or addition of one or more otherfeatures, integers, operations, elements, components, and/or groupsthereof. Terms such as first, second, A, B, (a), (b), and the like maybe used herein to describe components. Each of these terminologies isnot used to define an essence, order, or sequence of a correspondingcomponent but used merely to distinguish the corresponding componentfrom other component(s). For example, a first component may be referredto as a second component, and similarly the second component may also bereferred to as the first component.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains based onan understanding of the present disclosure. Terms, such as those definedin commonly used dictionaries, are to be interpreted as having a meaningthat is consistent with their meaning in the context of the relevant artand the present disclosure, and are not to be interpreted in anidealized or overly formal sense unless expressly so defined herein.

Hereinafter, some example embodiments will be described in detail withreference to the accompanying drawings. Regarding the reference numeralsassigned to the elements in the drawings, it should be noted that thesame elements will be designated by the same reference numerals,wherever possible, even though they are shown in different drawings.

FIG. 1 is a diagram illustrating an example of a bird-view image of avehicle according to an example embodiment.

To generate an elevated top view or a bird's eye view image (hereinaftersimply “bird-view image”) of a vehicle, it needs to merge a plurality ofimages captured by a plurality of cameras of the vehicle. To merge theimages, it needs to match coordinate systems of the images. That is, itneeds to generate partial bird-view images by transforming thecoordinate systems of the images into a common coordinate system that isset in advance for the vehicle, and generate a final bird-view image bymerging the generated partial bird-view images. Here, a partialbird-view image may be generated by warping an image, for example. Thegenerated final bird-view image may be used for an around view monitor(AVM). The term “bird-view image” used herein may refer to an imagecaptured from a bird's eye viewpoint.

In the example of FIG. 1, a generated bird-view image 100 may be animage of a vehicle 110 that is captured from a bird's eye viewpoint. Inthis example, the bird-view image 100 may not include an image of thevehicle 110. Alternatively, the bird-view image 100 may include a presetimage of the vehicle 110 that is not an actually captured image of thevehicle 110. The bird-view image 100 may include a left line 120 on aleft side of the vehicle 110, a right line 130 on a right side of thevehicle 110, a first marking 140, and a second marking 150. Thebird-view image 100 may further include a guardrail on a road, forexample.

However, due to an error occurring when installing a camera in avehicle, it may not be possible in reality to completely matchcoordinate axes of cameras to a common coordinate system. Although itmay be possible to precisely calibrate a camera installation error at afactory that produces a vehicle, the camera installation error maychange or occur constantly after the vehicle is released because aninstallation position of a camera in the vehicle changes by a physicalforce, for example, impact and twisting occurring while the vehicle istraveling. Thus, to generate an accurate bird-view image, a plurality ofcameras of a vehicle may need to be calibrated on a periodic basis oreach time an impact occurs.

To merge a plurality of images, it needs to perform camera calibrationto correct such a camera installation error first. For the cameracalibration, information including, for example, a height and an angleat which a camera is installed, may be required. To obtain suchinformation for the camera calibration, an existing method that installsa certain reference pattern such as a checkerboard on the ground andobtains camera information using a pattern image of the checkerboardcaptured by cameras of a vehicle has been used.

The existing method may obtain precise camera information because anaccurate relative position of a certain marker such as a pattern may beobtained in advance. However, to perform the method, such a certainpattern may need to be installed around a vehicle. In addition, themethod may not be performed while the vehicle is traveling.

In a case of a general vehicle, a camera installation error may occur ona periodic basis or each time an impact occurs due to an actual movementof the vehicle, and thus camera calibration may need to be automaticallyperformed even while the vehicle is traveling. The camera calibrationperformed while a vehicle is traveling may be referred to as on-roadcalibration (ORC).

When the calibration is performed only using a line on a road, it ispossible to estimate only rotation information of extrinsic parametersof a camera. The extrinsic parameters may include rotation informationand position information. For example, when only the rotationinformation is estimated while the position information is set as aninaccurate value, the estimated rotation information may be inaccurateas a result.

The rotation information may be estimated as a more inaccurate value tocorrect the position information set as an inaccurate value, and thusother portions excluding lines on a road may not be matched although thelines are well matched.

Thus, all patterns on a road, in addition to lines, may be used for moreaccurate camera calibration. The patterns on the road may includepatterns drawn in colors different from those indicating lanes on theroad to convey information to drivers, for example, lines, speed limitmarkings, left and right direction markings, and children protectionzones. The reasons for using the patterns may be that most of thepatterns are on the road and on the ground, and thus the patternsindicate same points in a bird-view image, and the patterns havedistinctive feature points.

Hereinafter, a method of calibrating a plurality of cameras, simplyreferred to herein as a camera calibration method, will be described indetail with reference to FIGS. 2 through 13.

FIGS. 2 and 3 are diagrams illustrating examples of rotation informationand position information of extrinsic parameters of cameras according toan example embodiment.

To calibrate cameras, extrinsic parameters of the cameras may need to becorrected or calibrated. The extrinsic parameters may include rotationinformation and position information of the cameras.

In the example of FIG. 2, an upper portion 210 illustrates a pitch (Rx)of a camera, a middle portion 220 illustrates a roll (Rz) of the camera,and a lower portion 230 illustrates a yaw (Ry) of the camera.

In the example of FIG. 3, an upper portion 310 and a lower portion 320illustrate position information (Tx, Ty, and Tz). The positioninformation may be set based on a position of a vehicle. For example, aworld coordinate system in which a certain position of the vehicle isset as a reference point may be set in advance, and position informationof cameras may be represented by the world coordinate system.

FIG. 4 is a diagram illustrating an example of an electronic deviceconfigured to calibrate a plurality of cameras according to an exampleembodiment.

Referring to FIG. 4, an electronic device 400 includes a communicator410, a processor 420, a memory 430, and a camera 440. The electronicdevice 400 may be provided in a vehicle. For example, the electronicdevice 400 may be a device such as an electronic control unit (ECU). Foranother example, the electronic device 400 may be an independent deviceconnected to an ECU.

The communicator 410 may be connected to the processor 420, the memory430, and the camera 440 to transmit and receive data. The communicator410 may be connected to another external device to transmit and receivedata thereto and therefrom. Hereinafter, the expression “transmit andreceive A” will indicate transmitting and receiving information or dataindicating A.

The communicator 410 may be embodied as a circuitry in the electronicdevice 400. For example, the communicator 410 may include an internalbus and an external bus. For another example, the communicator 410 maybe an element that connects the electronic device 400 and an externaldevice. The communicator 410 may be an interface. The communicator 410may receive data from the external device and transmit the received datato the processor 420 and the memory 430.

The processor 420 may process the data received by the communicator 410and data stored in the memory 430. The processor 420 may be a dataprocessing device that is embodied by hardware having a circuit of aphysical structure to execute desired operations. The desired operationsmay include, for example, a code or instructions included in a program.The data processing device embodied by hardware may include, forexample, a microprocessor, a central processing unit (CPU), a processorcore, a multi-core processor, a multiprocessor, an application-specificintegrated circuit (ASIC), and a field-programmable gate array (FPGA).

The processor 420 may execute a computer-readable code, for example,software, that is stored in the memory 430, and instructions derived bythe processor 420.

The memory 430 may store the data received by the communicator 410 anddata processed by the processor 420. For example, the memory 430 maystore a program, for example, an application and software. The programto be stored may be a set of syntaxes that is coded to calibrate camerasby adjusting extrinsic parameters of the cameras and is executable bythe processor 420.

The memory 430 may include at least one volatile memory, nonvolatilememory, random-access memory (RAM), flash memory, hard disk drive, andoptical disc drive.

The memory 430 may store a set of instructions that operates theelectronic device 400, for example, software. The set of instructionsthat operates the electronic device 400 may be executed by the processor420.

The camera 440 may generate an image by capturing a scene. The camera440 may include a plurality of cameras. For example, the cameras may beinstalled on a front side, a rear side, a left side, and a right side ofthe vehicle.

Hereinafter, the communicator 410, the processor 420, the memory 430,and the camera 440 will be described in detail with reference to FIGS. 5through 13.

FIG. 5 is a flowchart illustrating an example of a method of calibratinga plurality of cameras according to an example embodiment. The method ofcalibrating a plurality of cameras may also be referred to herein simplyas a camera calibration method.

Operations 510 through 560 to be described hereinafter with reference toFIG. 5 may be performed by the electronic device 400 described abovewith reference to FIG. 4.

Referring to FIG. 5, in operation 510, the electronic device 400 obtainsa plurality of images of surroundings of a vehicle. For example, theelectronic device 400 may obtain the images captured using the camera440. To detect feature points of a pattern present at a same time and asame position, the images may be captured at a same capturing time. Thepattern may indicate an object on a road.

The camera 440 may include a wide-angle lens having a greater viewingangle than a general lens, or a fish-eye lens which is asuper-wide-angle lens having a viewing angle of 180 degrees (°) orgreater. For example, using such a lens having a great viewing angle togenerate an image, it is possible to capture an image of all thesurroundings of the vehicle.

For example, when the camera 440 includes four cameras that capturerespectively an image of a front-side view from the vehicle, an image ofa rear-side view from the vehicle, an image of a left-side view from thevehicle, and an image of a right-side view from the vehicle, and cameracalibration is performed based on the images captured by the fourcameras, an extrinsic parameter of each of the cameras may be estimated.For example, a pitch (Rx), a yaw (Ry), and a roll (Rz) may be estimatedas rotation information of a camera. For another example, translationinformation (Tx, Ty) and height information (Tz) may be estimated asposition information of a camera.

In this example, when the camera 440 includes the four cameras thatcapture the images of the front-side view, the rear-side view, theleft-side view, and the right-side view of the vehicle, four channelsmay be defined for each of the cameras.

For example, when there is a pattern on a road that is capturedsimultaneously by at least two of the cameras, the pattern may need toindicate a same position in images that are transformed by a bird's eyeviewpoint. In this example, when an extrinsic parameter of each of thefour cameras is accurate, the pattern may be indicated as a single onein a bird-view image. However, when the extrinsic parameter is notaccurate, the pattern may be indicated as a plurality of patterns.

Thus, it is possible to estimate an accurate extrinsic parameter of eachof the cameras. Hereinafter, a method of estimating an accurateextrinsic parameter using obtained images will be described in detail.

In operation 520, the electronic device 400 sets a region of interest(ROI) in each of the images. The ROI may be a portion of an image thatoverlaps another image. Thus, for example, a first ROI of a first imageamong the images and a second ROI of a second image among the images mayinclude a common region. In this example, when the first image is afront-side view image and the second image is a left-side view image,the first ROI of the first image and the second ROI of the second imagemay be at a position indicating an upper left end of the vehicle. TheROI will be described hereinafter in detail with reference to FIGS. 6and 7.

In operation 530, the electronic device 400 detects one or more featurepoints of the set ROIs. For example, the electronic device 400 maydetect feature points of the first ROI and feature points of the secondROI. The electronic device 400 may detect feature points of the patternpresent on the road. For example, the feature points may include afeature point of a line on the road, a feature point of a directionmarking, and the like. To detect the feature points, a scale-invariantfeature transform (SIFT) algorithm, a speeded-up robust feature (SURF)algorithm, a Haar algorithm, a Ferns algorithm, and an oriented featuresfrom accelerated segment test (FAST) and rotated binary robustindependent elementary features (BRIEF) (ORB) algorithm may be used.However, examples are not limited to the example algorithms described inthe foregoing.

In operation 540, the electronic device 400 matches a first featurepoint of the first ROI and a second feature point of the second ROI. Forexample, a feature point of an ROI of a front-side view image may matcha feature point of an ROI of a left-side view image, the feature pointof the ROI of the front-side view image may match a feature point of anROI of a right-side view image, a feature point of an ROI of a rear-sideview image may match the feature point of the ROI of the left-side viewimage, and the feature point of the ROI of the rear-side view image maymatch the feature point of the ROI of the right-side view image.

In operation 550, the electronic device 400 calculates a first bird-viewcoordinate of the first feature point and a second bird-view coordinateof the second feature point. For example, when the first feature pointis a feature point of a front-side view image and the second featurepoint is a feature point of a left-side view image, the electronicdevice 400 may calculate the first bird-view coordinate of the firstfeature point based on an extrinsic parameter of a front camera andcalculate the second bird-view coordinate of the second feature pointbased on an extrinsic parameter of a left camera. The term “bird-viewcoordinate” described herein may indicate coordinates in a coordinatesystem of a bird-view image to be generated. For example, the coordinatesystem of the bird-view image may be a world coordinate system describedabove.

As a prerequisite for performing operation 550, whether the number ofmatching feature points is greater than or equal to a preset number maybe determined after operation 540 is performed. For example, when thenumber of matching feature points of the first ROI and the second ROI isgreater than or equal to the preset number, the first bird-viewcoordinate and the second bird-view coordinate may be calculated. As thenumber of the matching feature points of the first ROI and the secondROI increases, it is possible to estimate a more accurate extrinsicparameter.

However, when the number of the matching feature points of the first ROIand the second ROI is less than the preset number, accuracy in adjustingthe extrinsic parameter may not be guaranteed. Thus, the extrinsicparameter may not be adjusted for a plurality of current images, forexample, current frames.

In operation 560, the electronic device 400 calibrates the cameras byadjusting the extrinsic parameter of each of the cameras based on anerror between the first bird-view coordinate and the second bird-viewcoordinate.

When the cameras are not accurately calibrated, different worldcoordinate systems may be generated for the cameras. Thus, to replace anaccurate world coordinate system, a position of a neighboring cameraadjacent to a target camera may be used. For example, the cameracalibration may be performed by transforming the position of theneighboring camera into a position of the target camera, and setting thetransformed position as a reference point of the world coordinatesystem. Using such a method described in the foregoing, the cameracalibration may be performed using the reference point even though anaccurate reference point of the world coordinate system is not known,and it may thus be possible to adjust the extrinsic parameter.

However, when an extrinsic parameter of the neighboring camera isinaccurate, the set reference point of the world coordinate system mayalso be an inaccurate position. Thus, a method of repeatedly performingthe camera calibration on each of the cameras, not performing the cameracalibration once on one camera, may be used to gradually reduce an errorof the extrinsic parameter. As described above, by setting a referencepoint of a world coordinate system of each target camera while changingthe target camera, and performing repeatedly the camera calibrationusing the set reference point of the world coordinate system, it ispossible to calibrate a plurality of cameras simultaneously.

Hereinafter, the camera calibration method will be described in greaterdetail with reference to FIGS. 8 through 13.

FIG. 6 is a diagram illustrating examples of ROIs according to anexample embodiment.

Referring to FIG. 6, a first image 611 may be a front-side view imagegenerated by a front camera 610, or a first partial bird-view imagegenerated based on the front-side view image. A second image 621 may bea left-side view image generated by a left camera 620, or a secondpartial bird-view image generated based on the left-side view image. Athird image 631 may be a rear-side view image generated by a rear camera630, or a third partial bird-view image generated based on the rear-sideview image. A fourth image 641 may be a right-side view image generatedby a right camera 640, or a fourth partial bird-view image generatedbased on the right-side view image.

The images 611, 621, 631, and 641 are provided as examples to describeROIs around a vehicle 600. Thus, whether the images 611, 621, 631, and641 are images generated by cameras or transformed partial bird-viewimages is not limited to the examples described in the foregoing.

A first ROI may be set such that the first ROI includes at least aportion of a first common region 651 of the first image 611 and thesecond image 621. For example, the first ROI may include a leftmost sideof the front-side view image, and a second ROI may include an uppermostside of the left-side view image. Similarly, ROIs may be set such thatthe ROIs includes at least a portion of a second common region 652 ofthe second image 621 and the third image 631. ROIs may be set such thatthe ROIs includes at least a portion of a third common region 653 of thethird image 631 and the fourth image 641. ROIs may be set such that thefourth ROI includes at least a portion of a fourth common region 654 ofthe fourth image 641 and the first image 611.

FIG. 7 is a diagram illustrating an example of a method of extractingand matching feature points according to an example embodiment.

Referring to FIG. 7, a first ROI 712 may be set in a front-side viewimage 710 and a second ROI 722 may be set in a left-side view image 720.The electronic device 400 may generate partial images by cropping theset ROIs 712 and 722.

Based on the set ROIs 712 and 722 or the generated partial images, oneor more feature points 713, 714, 723, and 724 of the ROIs 712 and 722may be detected. Although only some feature points of direction markingsare illustrated for the convenience of description, a plurality of otherfeature points may be detected based on a text and an edge of an image.

The electronic device 400 may match the detected feature points. Forexample, the feature point 713 of the first ROI 712 may match thefeature point 723 of the second ROI 722, and the feature point 714 ofthe first ROI 712 may match the feature point 724 of the second ROI 722.

To match the feature points, various algorithms such as a SIFTalgorithm, a SURF algorithm, a Haar algorithm, a Ferns algorithm, and anORB algorithm may be used, but not limited thereto.

FIG. 8 is a flowchart illustrating an example of a method of calibratinga plurality of cameras by adjusting an extrinsic parameter of each ofcameras according to an example embodiment.

Operation 560 described above with reference to FIG. 5 may includeoperations 810 through 830 to be described hereinafter with reference toFIG. 8.

Referring to FIG. 8, in operation 810, the electronic device 400calculates a first error between the first bird-view coordinate and thesecond bird-view coordinate. When an extrinsic parameter of each of thecameras is set or adjusted accurately, the first bird-view coordinateand the second bird-view coordinate may be indicated the same, and thusthe first error may be 0 or a value close to 0.

The first bird-view coordinate and the second bird-view coordinate maybe associated with a pair of matching feature points. For example, whenthere is a plurality of pairs of matching feature points, an error maybe calculated for each of the pairs.

In operation 820, the electronic device 400 determines whether the firsterror is greater than or equal to a preset threshold value. When thefirst error is less than the threshold value, the camera calibration forcalibrating the cameras may be terminated. However, when one of errorsdoes not satisfy such a condition described in the foregoing even thoughanother error of the errors satisfies the condition, the cameracalibration may be performed continuously.

In operation 830, when the first error is greater than or equal to thethreshold value, the electronic device 400 adjusts an extrinsicparameter of at least one of the cameras such that the first error isminimized. For example, the electronic device 400 may adjust at leastone of rotation information or position information of the camera.

According to an example embodiment, based on a plurality of errors, acause of occurrence of the errors may be determined. The cause mayinclude, for example, inaccurate rotation information of a left camera.To solve such a cause, at least one of rotation information or positioninformation of a camera may be adjusted. For example, the rotationinformation of the left camera may be adjusted. To determine such acause, various methods may be used, and the methods may not be limitedto a specific one.

For example, for the camera calibration, a Levenberg-Marquardt algorithmand a gradient descent algorithm may be used.

FIG. 9 is a diagram illustrating an example of an error betweenbird-view coordinates of matching feature points according to an exampleembodiment.

When the feature point 713 and the feature point 723 match each other asillustrated in FIG. 7, a first bird-view coordinate 910 of the featurepoint 713 and a second bird-view coordinate 920 of the feature point 723may be calculated.

When an extrinsic parameter of each of cameras is accurate, the firstbird-view coordinate 910 and the second bird-view coordinate 920 mayhave a same coordinate in a world coordinate system. However, when theextrinsic parameter is not accurate, the first bird-view coordinate 910and the second bird-view coordinate 920 may have different coordinatesin the world coordinate system.

When accuracy of the extrinsic parameter decreases, a first error 930between the first bird-view coordinate 910 and the second bird-viewcoordinate 920 may increase. The first error 930 may be calculated as adistance therebetween. When the accuracy of the extrinsic parameterincreases through adjustment, the first error 930 may decrease.

FIG. 10 illustrates examples of partial bird-view images generated whena plurality of cameras are desirably calibrated according to an exampleembodiment.

Referring to FIG. 10, a feature point 1011 in a first partial bird-viewimage 1010 of a left-side view image may match a feature point 1021 in asecond partial bird-view image 1020 of a rear-side view image, andfeature points 1022 and 1023 in the second partial bird-view image 1020may respectively match feature points 1031 and 1032 in a third partialbird-view image 1030.

When a plurality of cameras are desirably calibrated, a coordinate ofthe feature point 1011 and a coordinate of the feature point 1021 maycorrespond to or match each other, for example, they may be the same oralmost the same, in a world coordinate system. Similarly, coordinates ofthe feature points 1022 and 1023 and coordinates of the feature points1031 and 1032 may correspond to or match each other, for example, theymay be the same or almost the same, in the world coordinate system.

FIG. 11 illustrates examples of partial bird-view images generated whena plurality of cameras are not desirably calibrated according to anexample embodiment.

Referring to FIG. 11, a feature point 1111 in a first partial bird-viewimage 1110 of a left-side view image may match a feature point 1121 in asecond partial bird-view image 1120 of a rear-side view image, andfeature points 1122 and 1123 in the second partial bird-view image 1110may respectively match feature points 1131 and 1132 in a third partialbird-view image 1130 of a right-side view image.

When a plurality of cameras are not desirably calibrated, a coordinateof the feature point 1111 and a coordinate of the feature point 1121 maynot correspond to or match each other in a world coordinate system.Similarly, coordinates of the feature points 1122 and 1123 andcoordinates of the feature points 1131 and 1132 may not correspond to ormatch each other in the world coordinate system.

Even when the coordinate of the feature point 1111 and the coordinate ofthe feature point 1121 correspond to or match each other, thecoordinates of the feature points 1122 and 1123 and the feature points1131 and 1132 may not correspond to or match each other. In such a case,an extrinsic parameter of each of cameras may be adjusted such that thecoordinates of the feature points 1122 and 1123 and the feature points1131 and 1132 correspond to or match each other.

FIG. 12 is a flowchart illustrating an example of a method ofcalculating an adjusted error between bird-view coordinates based on anadjusted extrinsic parameter according to an example embodiment.

After operation 560 described above with reference to FIG. 5 isperformed, operation 550 described above with reference to FIG. 5 may beperformed again. That is, by performing repeatedly operations 550 and560, it is possible to calibrate a plurality of cameras.

Operation 550 may further include operation 1210 to be describedhereinafter with reference to FIG. 12. In operation 1210, the electronicdevice 400 calculates a first adjusted bird-view coordinate of the firstfeature point and a second adjusted bird-view coordinate of the secondfeature point based on the adjusted extrinsic parameter.

Operation 560 may further include operation 1220 to be describedhereinafter with reference to FIG. 12. In operation 1220, the electronicdevice 400 calculates a first adjusted error between the first adjustedbird-view coordinate and the second adjusted bird-view coordinate.

Operation 1220 may correspond to operation 810 described above withreference to FIG. 8, and operations 820 and 830 described above withreference to FIG. 8 may be performed after operation 1220 is performed.

FIG. 13 is a flowchart illustrating an example of a method of adjustingan extrinsic parameter of a camera such that an error is minimizedaccording to an example embodiment. Operation 830 described above withreference to FIG. 8 may include operations 1310 and 1320 to be describedhereinafter with reference to FIG. 13.

Referring to FIG. 13, in operation 1310, the electronic device 400adjusts an extrinsic parameter of each of a front camera and a rearcamera.

In operation 1320, the electronic device 400 adjusts an extrinsicparameter of each of a left camera and a right camera.

An example of a method of extracting and matching feature points fromimages which are not bird-view images but, for example, fish-eye images,and transforming matching feature points by a bird's eye viewpoint, isdescribed above with reference to FIGS. 5 through 13.

Hereinafter, an example of a method of transforming a portion of animage which is not a bird-view image but, for example, a fish-eye image,into a bird-view image, extracting feature points from partial bird-viewimages, and matching the extracted feature points will be described withreference to FIGS. 14 through 18.

FIG. 14 is a flowchart illustrating an example of a method ofcalibrating a plurality of cameras according to another exampleembodiment. The method of calibrating a plurality of cameras may also bereferred to herein simply as a camera calibration method.

Operations 1410 through 1460 to be described hereinafter with referenceto FIG. 14 may be performed by the electronic device 400 described abovewith reference to FIG. 4.

Referring to FIG. 14, in operation 1410, the electronic device 400obtains a plurality of images of surroundings of a vehicle. The obtainedimages may be images captured using a fish-eye lens.

For a more detailed description of operation 1410, reference may be madeto the description of operation 510 provided above with reference toFIG. 5.

In operation 1420, the electronic device 400 determines a partial regionof each of the images based on an ROI set in advance with respect to abird's eye viewpoint. For example, a region corresponding to a frontleft side of an entire bird-view image to be generated may be preset asa first ROI, and a region of a front-side view image corresponding tothe first ROI may be determined to be a partial region. For anotherexample, a region corresponding to a front left side of an entirebird-view image to be generated may be preset as a second ROI, and aregion of a left-side view image corresponding to the second ROI may bedetermined to be a partial region. The first ROI, the second ROI, andthe partial regions respectively corresponding to the first ROI and thesecond ROI will be described hereinafter in detail with reference toFIGS. 15 and 16.

In operation 1430, the electronic device 400 generates a plurality ofpartial bird-view images by transforming the determined partial regionsof the images by the bird's eye viewpoint. The partial regions of theimages determined in operation 1420 may be images captured using afish-eye lens, and thus shapes in the images may be distorted. Bytransforming the partial regions by the bird's eye viewpoint, it ispossible to restore the shapes in the images. A partial bird-view imagewill be described hereinafter in detail with reference to FIG. 17.

In operation 1440, the electronic device 400 detects a first featurepoint of a first partial bird-view image and a second feature point of asecond partial bird-view image. For example, a feature point on apattern positioned on a road may be detected. To detect feature points,various algorithms, for example, a SIFT algorithm, a SURF algorithm, aHaar algorithm, a Ferns algorithm, and an ORB algorithm, may be used,but not be limited thereto.

In operation 1450, the electronic device 400 matches the first featurepoint and the second feature point. For example, the electronic device400 may match the first feature point of the first partial bird-viewimage of a front side and the second feature point of the second partialbird-view image of a left side. For another example, the electronicdevice 400 may match the first feature point of the first partialbird-view image of a left side and the second feature point of thesecond partial bird-view image of a rear side. When the images capturedusing a fish-eye lens are transformed into bird-view images, accuracy inmatching feature points in ROIs for same features may increase.

In operation 1460, the electronic device 400 calibrates a plurality ofcameras by adjusting an extrinsic parameter of each of the cameras basedon an error between a first bird-view coordinate of the first featurepoint and a second bird-view coordinate of the second feature point.

For a more detailed description of operation 1460, reference may be madeto the description of operation 560 provided above with reference toFIG. 5.

As a prerequisite for performing operation 560, whether the number ofmatching feature points is greater than or equal to a preset number maybe determined after operation 550 described above with reference to FIG.5 is performed. For example, when the number of matching feature pointsof the first partial bird-view image and the second partial bird-viewimage is greater than or equal to the preset number, camera calibrationmay be performed. When the number of matching feature points increases,an extrinsic parameter may be estimated more accurately.

However, when the number of the matching feature points of the firstpartial bird-view image and the second partial bird-view image is lessthan the preset number, accuracy in adjusting the extrinsic parametermay not be guaranteed, and thus the extrinsic parameter may not beadjusted for a plurality of current images, for example, current frames.

Hereinafter, the camera calibration method will be described in greaterdetail with reference to FIG. 18.

FIGS. 15 and 16 are diagrams illustrating examples of a partial regionof an image determined based on a preset ROI in a bird-view imageaccording to an example embodiment.

A right portion of FIG. 15 illustrates a front region 1510 of an entirebird-view image, and a portion of the front region 1510 may be set inadvance as an ROI 1511.

A left portion of FIG. 15 illustrates a front-side view image 1520 amonga plurality of images, and the front-side view image 1520 may be animage captured using a fish-eye lens. A partial region 1521 of thefront-side view image 1520 that corresponds to the ROI 1511 may bedetermined. For example, the partial region 1521 corresponding to theROI 1511 may be determined based on an extrinsic parameter of a frontcamera. When the extrinsic parameter of the front camera is adjusted,the partial region 1521 corresponding to the ROI 1511 may change.

Similarly, a right portion of FIG. 16 illustrates a left region 1610 ofan entire bird-view image, and a portion of the left region 1610 may beset in advance as an ROI 1611.

A left portion of FIG. 16 illustrates a left-side view image 1620 amonga plurality of images, and the left-side view image 1620 may be an imagecaptured using a fish-eye lens. A partial region 1621 of the left-sideview image 1620 that corresponds to the ROI 1611 may be determined. Forexample, the partial region 1621 corresponding to the ROI 1611 may bedetermined based on an extrinsic parameter of a left camera. When theextrinsic parameter of the left camera is adjusted, the partial region1621 corresponding to the ROI 1611 may change.

In such a relationship between the ROI 1511 and the ROI 1611, the ROI1511 may be referred to herein as a first ROI and the ROI 1611 may bereferred to herein as a second ROI.

FIG. 17 is a diagram illustrating an example of a method of extractingand matching feature points of partial bird-view images according to anexample embodiment.

Referring to FIG. 17, the partial region 1521 of FIG. 15 and the partialregion 1621 of FIG. 16 may be transformed into a first partial bird-viewimage 1710 and a second partial bird-view image 1720, respectively. Thefirst partial bird-view image 1710 and the second partial bird-viewimage 1720 may be images from a bird's eye viewpoint.

One or more feature points 1711 and 1712 of the first partial bird-viewimage 1710 may be extracted. One or more feature points 1721 and 1722 ofthe second partial bird-view image 1720 may be extracted. The featurepoint 1711 may match the feature point 1721, and the feature point 1712may match the feature point 1722.

FIG. 18 is a flowchart illustrating an example of a method ofcalibrating a plurality of cameras by adjusting an extrinsic parameterof each of the cameras based on an error between a first bird-viewcoordinate and a second bird-view coordinate according to an exampleembodiment.

Operation 1460 described above with reference to FIG. 14 may includeoperations 1810 through 1830 to be described hereinafter with referenceto FIG. 18.

Referring to FIG. 18, in operation 1810, the electronic device 400calculates a first error between the first bird-view coordinate and thesecond bird-view coordinate. When an extrinsic parameter of a camera isaccurately set or adjusted, the first bird-view coordinate and thesecond bird-view coordinate may be indicated the same, and thus thefirst error may be 0 or a value close to 0.

The first bird-view coordinate and the second bird-view coordinate maybe associated with a pair of matching feature points. When there is aplurality of pairs of matching feature points, an error may becalculated for each of the pairs.

In operation 1820, the electronic device 400 determines whether thefirst error is greater than or equal to a preset threshold value. Whenthe first error is less than the threshold value, camera calibration forcalibrating a plurality of cameras may be terminated. However, when oneof errors does not satisfy such a condition described in the foregoingeven though another error of the errors satisfies the condition, thecamera calibration may be performed continuously.

In operation 1830, when the first error is greater than or equal to thethreshold value, the electronic device 400 adjusts an extrinsicparameter of at least one of the cameras such that the first error isminimized. For example, the electronic device 400 may adjust at leastone of rotation information or position information of the camera.

According to an example embodiment, based on a plurality of errors, acause of occurrence of the errors may be determined. The cause mayinclude, for example, inaccurate rotation information of a left camera.To solve such a cause, at least one of rotation information or positioninformation of a camera may be adjusted. For example, the rotationinformation of the left camera may be adjusted. To determine such acause, various methods may be used, and the methods may not be limitedto a specific one.

For example, for the camera calibration, a Levenberg-Marquardt algorithmand a gradient descent algorithm may be used.

According to an example embodiment described herein, there is provided amethod and device for calibrating a plurality of cameras.

According to another example embodiment described herein, there isprovided a method and device for calibrating a plurality of cameraswhile a vehicle is traveling. The units described herein may beimplemented using hardware components and software components. Forexample, the hardware components may include microphones, amplifiers,band-pass filters, audio to digital convertors, non-transitory computermemory and processing devices. A processing device may be implementedusing one or more general-purpose or special purpose computers, such as,for example, a processor, a controller and an arithmetic logic unit(ALU), a digital signal processor, a microcomputer, a field programmablegate array (FPGA), a programmable logic unit (PLU), a microprocessor orany other device capable of responding to and executing instructions ina defined manner. The processing device may run an operating system (OS)and one or more software applications that run on the OS. The processingdevice also may access, store, manipulate, process, and create data inresponse to execution of the software. For purpose of simplicity, thedescription of a processing device is used as singular; however, oneskilled in the art will appreciated that a processing device may includemultiple processing elements and multiple types of processing elements.For example, a processing device may include multiple processors or aprocessor and a controller. In addition, different processingconfigurations are possible, such a parallel processors.

The software may include a computer program, a piece of code, aninstruction, or some combination thereof, to independently orcollectively instruct or configure the processing device to operate asdesired. Software and data may be embodied permanently or temporarily inany type of machine, component, physical or virtual equipment, computerstorage medium or device, or in a propagated signal wave capable ofproviding instructions or data to or being interpreted by the processingdevice. The software also may be distributed over network coupledcomputer systems so that the software is stored and executed in adistributed fashion. The software and data may be stored by one or morenon-transitory computer readable recording mediums. The non-transitorycomputer readable recording medium may include any data storage devicethat can store data which can be thereafter read by a computer system orprocessing device.

The methods according to the above-described example embodiments may berecorded in non-transitory computer-readable media including programinstructions to implement various operations of the above-describedexample embodiments. The media may also include, alone or in combinationwith the program instructions, data files, data structures, and thelike. The program instructions recorded on the media may be thosespecially designed and constructed for the purposes of exampleembodiments, or they may be of the kind well-known and available tothose having skill in the computer software arts. Examples ofnon-transitory computer-readable media include magnetic media such ashard disks, floppy disks, and magnetic tape; optical media such asCD-ROM discs, DVDs, and/or Blue-ray discs; magneto-optical media such asoptical discs; and hardware devices that are specially configured tostore and perform program instructions, such as read-only memory (ROM),random access memory (RAM), flash memory (e.g., USB flash drives, memorycards, memory sticks, etc.), and the like. Examples of programinstructions include both machine code, such as produced by a compiler,and files containing higher level code that may be executed by thecomputer using an interpreter. The above-described devices may beconfigured to act as one or more software modules in order to performthe operations of the above-described example embodiments, or viceversa.

While this disclosure includes specific examples, it will be apparent toone of ordinary skill in the art that various changes in form anddetails may be made in these examples without departing from the spiritand scope of the claims and their equivalents. The examples describedherein are to be considered in a descriptive sense only, and not forpurposes of limitation. Descriptions of features or aspects in eachexample are to be considered as being applicable to similar features oraspects in other examples. Suitable results may be achieved if thedescribed techniques are performed in a different order, and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner and/or replaced or supplemented by othercomponents or their equivalents.

Therefore, the scope of the disclosure is defined not by the detaileddescription, but by the claims and their equivalents, and all variationswithin the scope of the claims and their equivalents are to be construedas being included in the disclosure.

What is claimed is:
 1. A camera calibration method to be performed by aprocessor provided in a vehicle, comprising: obtaining a plurality ofimages of surroundings of the vehicle captured by a plurality of camerasof the vehicle; setting a region of interest (ROI) in each of theimages, wherein a first ROI of a first image among the images and asecond ROI of a second image among the images include a common region;detecting one or more feature points of the set ROIs; matching a firstfeature point of the first ROI and a second feature point of the secondROI based on the detected feature points; calculating a first bird-viewcoordinate of the first feature point and a second bird-view coordinateof the second feature point; and calibrating the cameras by adjusting anextrinsic parameter of each of the cameras based on an error between thefirst bird-view coordinate and the second bird-view coordinate.
 2. Thecamera calibration method of claim 1, wherein the cameras include atleast two of a left camera configured to capture an image of a left-sideview from the vehicle, a right camera configured to capture an image ofa right-side view from the vehicle, a front camera configured to capturean image of a front-side view from the vehicle, or a rear cameraconfigured to capture an image of a rear-side view from the vehicle. 3.The camera calibration method of claim 1, wherein the extrinsicparameter of each of the cameras includes rotation information andposition information of each of the cameras.
 4. The camera calibrationmethod of claim 1, wherein the feature points are associated with anobject on a road on which the vehicle is traveling.
 5. The cameracalibration method of claim 1, wherein the setting of the ROI in each ofthe images comprises: when the first image is a front-side view imageand the second image is a left-side view image, setting the ROI in eachof the images such that the first ROI includes a leftmost side of thefront-side view image and the second ROI includes a rightmost side ofthe left-side view image.
 6. The camera calibration method of claim 1,further comprising: generating partial images by cropping the set ROIs,wherein the detecting of the feature points of the ROIs comprises:detecting the one or more feature points of the ROIs based on thegenerated partial images.
 7. The camera calibration method of claim 1,wherein the calculating of the first bird-view coordinate of the firstfeature point and the second bird-view coordinate of the second featurepoint comprises: when the number of matching feature points of the firstROI and the second ROI is greater than or equal to a preset number,calculating the first bird-view coordinate and the second bird-viewcoordinate.
 8. The camera calibration method of claim 1, wherein thecalibrating of the cameras comprises: calculating a first error betweenthe first bird-view coordinate and the second bird-view coordinate; andadjusting an extrinsic parameter of at least one of the cameras suchthat the first error is minimized.
 9. The camera calibration method ofclaim 8, wherein the calculating of the first bird-view coordinate ofthe first feature point and the second bird-view coordinate of thesecond feature point comprises: when the extrinsic parameter of the atleast one of the cameras is adjusted, calculating a first adjustedbird-view coordinate of the first feature point and a second adjustedbird-view coordinate of the second feature point based on the adjustedextrinsic parameter, wherein the calibrating of the cameras comprises:calculating a first adjusted error between the first adjusted bird-viewcoordinate and the second adjusted bird-view coordinate.
 10. The cameracalibration method of claim 8, wherein the calibrating of the camerasfurther comprises: when the first error is less than a preset thresholdvalue, terminating the calibrating of the cameras.
 11. A non-transitorycomputer-readable storage medium storing instructions that, whenexecuted by a processor, cause the processor to perform the cameracalibration method of claim
 1. 12. An electronic device provided in avehicle to calibrate a plurality of cameras, comprising: a memory inwhich a program of calibrating the cameras is recorded; and a processorconfigured to perform the program, wherein the program comprises:obtaining a plurality of images of surroundings of the vehicle capturedby the cameras of the vehicle; setting a region of interest (ROI) ineach of the images, wherein a first ROI of a first image among theimages and a second ROI of a second image among the images include acommon region; detecting one or more feature points of the set ROIs;matching a first feature point of the first ROI and a second featurepoint of the second ROI based on the detected feature points;calculating a first bird-view coordinate of the first feature point anda second bird-view coordinate of the second feature point; andcalibrating the cameras by adjusting an extrinsic parameter of each ofthe cameras based on an error between the first bird-view coordinate andthe second bird-view coordinate.
 13. The electronic device of claim 12,wherein the feature points are associated with an object on a road onwhich the vehicle is traveling.
 14. The electronic device of claim 12,wherein the program further comprises: generating partial images bycropping the set ROIs, wherein the detecting of the feature points ofthe ROIs comprises: detecting the feature points of the ROIs based onthe generated partial images.
 15. The electronic device of claim 12,wherein the calculating of the first bird-view coordinate of the firstfeature point and the second bird-view coordinate of the second featurepoint comprises: when the number of matching feature points of the firstROI and the second ROI is greater than or equal to a preset number,calculating the first bird-view coordinate and the second bird-viewcoordinate.
 16. The electronic device of claim 12, wherein thecalibrating of the cameras comprises: calculating a first error betweenthe first bird-view coordinate and the second bird-view coordinate; andadjusting an extrinsic parameter of at least one of the cameras suchthat the first error is minimized.
 17. The electronic device of claim16, wherein the calculating of the first bird-view coordinate of thefirst feature point and the second bird-view coordinate of the secondfeature point comprises: when the extrinsic parameter of the at leastone of the cameras is adjusted, calculating a first adjusted bird-viewcoordinate of the first feature point and a second adjusted bird-viewcoordinate of the second feature point based on the adjusted extrinsicparameter, wherein the calibrating of the cameras comprises: calculatinga first adjusted error between the first adjusted bird-view coordinateand the second adjusted bird-view coordinate.
 18. The electronic deviceof claim 16, wherein the calibrating of the cameras comprises: when thefirst error is less than a preset threshold value, terminating thecalibrating of the cameras.
 19. A camera calibration method to beperformed by a processor provided in a vehicle, comprising: obtaining aplurality of images of surroundings of the vehicle captured by aplurality of cameras of the vehicle; determining a partial region ofeach of the images based on a region of interest (ROI) that is set inadvance with respect to a bird's eye viewpoint; generating a pluralityof partial bird-view images by transforming the determined partialregions by the bird's eye viewpoint, wherein a first partial bird-viewimage among the partial bird-view images corresponds to a first ROI anda second partial bird-view image among the partial bird-view imagescorresponds to a second ROI, and the first ROI and the second ROIinclude a common region; detecting a first feature point of the firstpartial bird-view image and a second feature point of the second partialbird-view image; matching the first feature point and the second featurepoint; and calibrating the cameras by adjusting an extrinsic parameterof each of the cameras based on an error between a first bird-viewcoordinate of the first feature point and a second bird-view coordinateof the second feature point.
 20. The camera calibration method of claim19, wherein the cameras include at least two of a left camera configuredto capture an image of a left-side view from the vehicle, a right cameraconfigured to capture an image of a right-side view from the vehicle, afront camera configured to capture an image of a front-side view fromthe vehicle, or a rear camera configured to capture an image of arear-side view from the vehicle.
 21. The camera calibration method ofclaim 19, wherein the calibrating of the cameras by adjusting theextrinsic parameter of each of the cameras based on the error betweenthe first bird-view coordinate of the first feature point and the secondbird-view coordinate of the second feature point comprises: when thenumber of matching feature points of the first partial bird-view imageand the second partial bird-view image is greater than or equal to apreset number, calibrating the cameras.
 22. The camera calibrationmethod of claim 19, wherein the calibrating of the cameras by adjustingthe extrinsic parameter of each of the cameras based on the errorbetween the first bird-view coordinate of the first feature point andthe second bird-view coordinate of the second feature point comprises:calculating a first error between the first bird-view coordinate and thesecond bird-view coordinate; and adjusting an extrinsic parameter of atleast one of the cameras such that the first error is minimized.
 23. Anon-transitory computer-readable storage medium storing instructionsthat, when executed by a processor, cause the processor to perform thecamera calibration method of claim
 19. 24. An electronic device providedin a vehicle to calibrate a plurality of cameras, comprising: a memoryin which a program of calibrating the cameras is recorded; and aprocessor configured to perform the program, wherein the programcomprises: obtaining a plurality of images of surroundings of thevehicle captured by the cameras of the vehicle; determining a partialregion of each of the images based on a region of interest (ROI) that isset in advance with respect to a bird's eye viewpoint; generating aplurality of partial bird-view images by transforming the determinedpartial regions by the bird's eye viewpoint, wherein a first partialbird-view image among the partial bird-view images corresponds to afirst ROI and a second partial bird-view image among the partialbird-view images corresponds to a second ROI, and the first ROI and thesecond ROI include a common region; detecting a first feature point ofthe first partial bird-view image and a second feature point of thesecond partial bird-view image; matching the first feature point and thesecond feature point; and calibrating the cameras by adjusting anextrinsic parameter of each of the cameras based on an error between afirst bird-view coordinate of the first feature point and a secondbird-view coordinate of the second feature point.